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Source Free Object Detection
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Source-Free Object Detection (SFOD) is a domain adaptation challenge in which only the pretrained source model weights are available during adaptation, with no access to the source data. The model must adapt solely using unlabeled samples from the target domain
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Context Aware Grounded Teacher for Source Free Object Detection
Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection
Enhancing Source-Free Domain Adaptive Object Detection with Low-confidence Pseudo Label Distillation
Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights
Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation
Source-free Domain Adaptive Object Detection in Remote Sensing Images
CLIP-Guided Source-Free Object Detection in Aerial Images
Periodically Exchange Teacher-Student for Source-Free Object Detection
Exploiting Low-confidence Pseudo-labels for Source-free Object Detection
Adversarial Alignment for Source Free Object Detection
Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection
Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data
Unbiased Mean Teacher for Cross-domain Object Detection
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
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